optimap.phase¶
Functions for computing, filtering, and analyzing phase maps from videos, and for detecting phase singularities.
- optimap.phase.compute_phase(video, offset=-0.5)[source]¶
Computes phase using Hilbert transformation, takes a normalized video or time-series as input.
- Parameters:
video ({t, x, y} ndarray) – normalized video
offset (float) – offset to add to video before computing phase, default is -0.5
- Returns:
phase in range [-pi, pi]
- Return type:
{t, x, y} ndarray
- optimap.phase.detect_phase_singularities(phase: ndarray, separate_by_charge=False)[source]¶
Detect phase singularities in a phase map video using [Iyer and Gray, 2001] method. Unlike [Iyer and Gray, 2001], the phase integral used here is only over four pixels.
TODO: verify if positions are (x, y) or (y, x)
- Parameters:
phase (ndarray) – Phase map video, shaped {t, x, y}
separate_by_charge (bool, optional) – Whether to separate phase singularities by charge, by default False See return value for details.
- Returns:
list of tuples – List of phase singularities for each frame (if separate_by_charge is False)
(list of tuples, list of tuples) – (positive_ps_list, negative_ps_list) (if separate_by_charge is True)
- optimap.phase.phasefilter_angle_threshold(phase: ndarray, wx: int, wy: int, wt: int, tr_angle: float, mask: ndarray | None = None)[source]¶
Remove outliers in a phase video by comparing them against their neighbors in space and time.
- Parameters:
- Returns:
phase video with outliers removed
- Return type:
{t, x, y} ndarray
- optimap.phase.phasefilter_disc(phase: ndarray, wx: int, wy: int, wt: int, mask: ndarray | None = None)[source]¶
TODO: Docstring for phasefilter_disc.